Russian Scientists Develop AI-Powered Resource-Saving Technology
Researchers at Tyumen State University, working with colleagues from Syria and Mexico, have created an energy-efficient method for processing associated petroleum gas that would otherwise be burned off.
At Tyumen State University, machine learning algorithms capable of solving problems by analyzing large sets of similar cases have been used to design more efficient methods for processing hydrocarbon feedstock.
One of the university’s flagship projects is an energy-saving autothermal technology for reverse-process hydrogenolysis of associated petroleum gas (APG). Instead of flaring, the process converts APG into valuable petrochemical products.
Energy efficiency is achieved because the reaction maintains the required temperature through its own heat release. This approach increases processing efficiency and reduces the need for auxiliary equipment. The effectiveness of the technology has already been confirmed in successful experiments.
The project is a joint effort with researchers from Syria and Mexico. In addition to machine learning, the Tyumen team employs advanced computer modeling of physical processes using COMSOL Multiphysics and Ansys CFD software.
